Detecting anomalous journal entries in a company's general ledger is essential for external auditors. An increasing trend employs outlier detection (OD) methods, especially machine learning methods, for anomaly detection in journal entry data. Recent research often lacks comparative analysis of OD methods. Thus, this study provides a comparative analysis of OD methods for journal entry anomaly detection using real-world accounting data. Additionally, in the context of domain-specific data preprocessing, we give special consideration to the amount, due to its importance for auditors. This yields three different dataset variants. We conduct our analysis based on three example accounts manually labeled by external auditors. Autoencoders, clustering-based local outlier factor (CBLOF), and histogram-based outlier score (HBOS) consistently outperform other methods across different accounts and dataset variants. With the provided results, this research enhances the understanding and applicability of OD methods for journal entry anomaly detection.

Smart contracts are code-based representations of business logic that encapsulate predefined rules that they verify and execute autonomously. As self-executing agreements, these contracts running on a blockchain facilitate interactions between untrusted parties, eliminating the need for intermediaries. They speed processes, increase transparency, and facilitate data exchange across national and organizational boundaries, providing a competitive advantage. However, there are significant costs associated with implementing smart contracts. In our study, we conduct a comparative analysis of these costs and compared them to traditional contracts. Using a combination of literature review and expert interviews, we dissect the cost structures of both approaches at an abstract level. We then construct a matrix to systematically compare traditional contracts with smart contracts. This framework allows us to model the conditions that favor the use of smart contracts over traditional ones, and to identify instances where each approach has a distinct advantage.

The emergence of decentralized finance and crypto assets has fundamentally changed the financial world and offers new potential to private investors and financial service providers. Despite the benefits, it is essential to face the risks. While initial regulation is already in place for risk management, financial service providers are often left to implement these measures on their own. This paper aims to identify the requirements for crypto assets risk management for financial service providers that go beyond implementing individual regulations. Guided by our research approach, we derive requirements from an academic and practical knowledge base. Accordingly, we evaluate the final requirements with a mixed-methods approach to receive feedback from portfolio managers, financial advisors, and blockchain experts. Finally, we will provide a comprehensive framework for financial service providers to effectively manage and mitigate crypto asset risks.